Find Academic Supervisors

Roland BüchiView profile

Professor

• Control Systems Engineering
• Robotics
• Artificial Intelligence
• +4 more

Prof. Dr. Roland Büchi is a Professor at the School of Engineering, Zurich University of Applied Sciences (ZHAW), specializing in control systems engineering and applied AI. His affiliation includes leadership roles in research projects such as the ongoing 'Digital Bridge to Computer Science' initiative. With a career spanning decades, he maintains active research output and industrial collaborations. Research Interests: Büchi focuses on control systems optimization , particularly PID controller tuning using AI methods, system identification, and applications in robotics and drone technology. His work bridges theoretical control theory with practical implementations in mechatronics. Recent efforts explore machine learning for hysteresis modeling and swarm optimization for control systems. Publication Trends: His 15 most recent works (2018-2024) emphasize AI-driven control optimization , drone technology advancements, and engineering education. Dominant themes include PID parameter tuning for time-delayed systems, drone telemetry, and adaptive learning algorithms. A notable shift toward AI/ML applications in control engineering emerged post-2020. Labs and Teams: Büchi collaborates with researchers like Lukas Gruber and has historical ties to ETH Zurich’s robotics projects. His work involves experimental validation at ZHAW’s engineering facilities, with patents in turbocharger magnetic bearing systems.

Sean CaoView profile

Associate Professor

• Artificial Intelligence in Finance
• Machine Learning Applications
• Capital Market Research
• +5 more

Sean Cao serves as Associate Professor (with tenure) at the Robert H. Smith School of Business, University of Maryland, where he is Director and Co-founder of the AI Initiative for Capital Market Research. He also holds an affiliation as professor at Harvard Business School's D 3 Institute. His academic journey began with a Ph.D. from the University of Illinois at Urbana-Champaign. Dr. Cao's research focuses on the intersection of artificial intelligence and capital markets, with particular expertise in how machine learning transforms financial analysis, corporate disclosure practices, and investment decision-making. His work examines the evolving relationship between human analysts and AI systems, blockchain applications in financial reporting, and the strategic adaptation of corporate communications for machine readership. He has pioneered research on the "AI divide" among investor groups and developed frameworks for human-AI collaborative stock analysis. His publication portfolio spans top journals including Journal of Financial Economics, Review of Financial Studies, Journal of Accounting Research, and Management Science. The research demonstrates consistent thematic progression toward increasingly sophisticated AI applications in finance, with recent work exploring distributed ledger technologies for auditing, machine learning for extracting private information from disclosures, and the economics of greenwashing in ESG funds. His studies frequently combine textual analysis with traditional financial metrics to uncover novel market insights. Fama-DFA Prize from Journal of Financial Economics for best paper in capital markets and asset pricing Michael J. Brennan Award from Review of Financial Studies Deloitte Initiative for AI and Learning award for developing trustworthy AI for social equity PanAgora Asset Management's Dr. Richard A. Crowell Memorial Prize Multiple best paper awards from Midwest Finance Association, Global AI Finance Conference, and Asian Finance Association Dr. Cao has delivered over 200 invited research talks at major institutions including the Central Bank of Japan, Central Bank of Thailand, and U.S. Securities and Exchange Commission. He serves as Guest Associate Editor for Management Science and has co-chaired Review of Financial Studies conferences on FinTech and Machine Learning. His educational initiatives include a widely adopted free AI textbook for finance and accounting that has been implemented at universities worldwide including Indiana University, UT Dallas, and University of Minnesota. As Director of the AI Initiative for Capital Market Research, Dr. Cao leads a multidisciplinary team exploring practical AI applications in finance. The initiative has secured significant funding including a $150,000 grant from GRF CPAs & Advisors. His research group maintains strong industry connections through partnerships with regulatory bodies, financial institutions, and technology companies, facilitating the translation of academic research into practical financial applications.

Bo DaiView profile

Assistant Professor

• Reinforcement Learning
• Machine Learning
• Data-Driven Decision Making
• +3 more

Bo Dai is an Assistant Professor at the School of Computational Science and Engineering, Georgia Institute of Technology, and a Staff Research Scientist at Google DeepMind. His research focuses on Agent AI, Generative Models, and Representation Learning, aiming to create decision-making agents through world modeling. He holds a Ph.D. from Georgia Tech (2013–2018) and previously worked at Google Brain. Dai has authored numerous influential papers in top conferences like NeurIPS, ICML, and ICLR, and received the AISTATS Best Paper Award (2016). Education: Ph.D., School of Computational Science and Engineering, Georgia Tech (2013–2018) Research Interests: Reinforcement Learning and Representation Learning for decision-making agents Generative Models and their integration with Agent AI Provable and scalable algorithms for real-world applications His work bridges theory and practice, emphasizing spectral representations and provable guarantees in complex systems. Key Article Trends: His recent work emphasizes scalable spectral methods for multi-agent systems, diffusion policies, and representation-based techniques in reinforcement learning. He also explores LLM alignment and sim-to-real transfer learning. Awards: AISTATS Best Paper Award (2016) NeurIPS Workshop Best Paper (2017) Ross Fellowship (2011–2012) Advising & Grants: Supervises 6 current Ph.D. and M.S. students. Active in organizing workshops on reinforcement learning and serves as an Area Chair for top conferences. Labs & Software: Leads development of Representation-based Reinforcement Learning and Repr-Control toolboxes for nonlinear control and stochastic systems.

• Digital Archaeology
• Computational Archaeology
• Machine Learning in Archaeology
• +13 more

Karsten Lambers is Professor of Digital and Computational Archaeology at the Faculty of Archaeology, Leiden University, where he leads research and teaching in the application of computational methods to archaeological data. His work integrates machine learning, remote sensing, text mining, and citizen science to advance archaeological prospection and heritage management. He is affiliated with the Department of Archaeological Sciences and plays key roles in research groups and university-wide initiatives such as SAILS and ARCHON. His research interests span Digital Archaeology , Machine Learning in Archaeology , Remote Sensing , Geoarchaeology , and Human-Environment Interaction . He investigates how computational tools can extract meaningful archaeological information from large datasets, including LiDAR imagery and excavation reports. His fieldwork spans Central Europe and Latin America, with a focus on prehistoric landscapes and cultural heritage. The analysis of his recent publications reveals a strong trend toward automated detection using deep learning (e.g., R-CNN, WODAN), named entity recognition in archaeological texts (e.g., ArcheoBERTje), and citizen science integration for data validation. His work bridges archaeology with computer science, geomatics, and environmental science, emphasizing interdisciplinary collaboration and methodological rigor. His scientific awards include: Best Thesis Award (University of Zurich, 2005) EUROPA NOSTRA Award (2020, 2022) Membership in the German Archaeological Institute (since 2022) Lambers actively supervises students and leads major research projects such as ABMA, EXALT, and Heritage Quest. He has secured substantial research funding and collaborates widely with computer scientists, geophysicists, and palaeoecologists. His teaching includes digital methods, modeling, and simulation, often linked to ongoing research. He has also contributed to open educational resources and digital textbooks in archaeology. He leads or participates in several research labs and teams, including the Digital Archaeology Research Group (which he chairs), the Heritage Quest citizen science project, and interdisciplinary teams focusing on alpine terraces and Iraqi prospection. His work emphasizes the integration of digital tools into practical archaeological workflows, advocating for complementary human-computer strategies.

James A. EvansView profile

Professor

• Computational Social Science
• Collective Cognition
• Innovation Studies
• +12 more

James A. Evans is a Professor at the University of Chicago, where he serves as Director of the Knowledge Lab and Faculty Director of the Masters Program in Computational Social Science. He is also an External Professor at the Santa Fe Institute. His research bridges computational methods with social theory to analyze collective cognition, innovation, and knowledge production across science, technology, and broader societal domains. Director, Knowledge Lab Faculty Director, Masters Program in Computational Social Science External Professor, Santa Fe Institute Evans’s research explores how social and technical institutions shape discovery processes, utilizing machine learning, network modeling, and large-scale data analysis. His work spans fields like computational social science, sociology of science, and data science, focusing on team dynamics, peer review, and the global structure of scholarship. His recent publications examine team size effects on innovation, discursive influence in academia, and the interplay between tradition and novelty in research strategies. Articles trend toward interdisciplinary approaches combining social theory, computational methods, and science policy. Evans supports novel observatories for human understanding through crowdsourcing, sensor networks, and semantic modeling. He has received funding from the National Science Foundation, National Institutes of Health, and Air Force Office of Scientific Research, with findings featured in major media outlets like Nature , Science , and The New York Times .

Julia TiconaView profile

Assistant Professor

• Digital Communication Technologies
• Precarious Work
• Algorithmic Labor Platforms
• +7 more

Julia Ticona is an Assistant Professor at the Annenberg School for Communication and holds a secondary appointment in Sociology at the University of Pennsylvania. Her research examines how digital communication technologies shape precarious work, focusing on mobile phones, algorithmic platforms, and data-driven management systems affecting low-wage workers. She explores identity construction, inequality, and labor dynamics in the gig economy, including a book under contract with Oxford University Press titled *The Digital Hustle: Precarity Beyond Platforms*. Dr. Ticona earned her Ph.D. in Sociology from the University of Virginia (2016), an M.A. from the same institution (2011), and a B.A. from Wellesley College (2009). She has held postdoctoral roles at Data & Society Research Institute and is currently a Faculty Affiliate there and an Associate Fellow at the Institute for Advanced Studies in Culture. Her work appears in journals like *New Media & Society* and outlets such as *Wired* and *Slate*. Her research interests include digital labor platforms, algorithmic management, carework regulation, and the intersection of technology with inequality. She has contributed to legal advocacy, including an amicus brief for the U.S. Supreme Court case *Carpenter v. U.S.*. Her courses address digital inequalities, labor in the digital economy, and qualitative research methods. Her publications analyze topics like visibility regimes in domestic work platforms, contested governance on carework platforms, and strategies for coping with insecure digital labor. Her recent work critiques data colonialism and explores how technological systems exacerbate socioeconomic divides. Leveraging qualitative methods, Ticona’s scholarship bridges sociological theory with empirical studies of digital labor. She collaborates across disciplines to address systemic inequities perpetuated by technology, emphasizing worker agency and institutional accountability in platform economies.

Eugenia RhoView profile

Assistant Professor

• Data Analytics
• Machine Learning
• Natural Language Processing
• +5 more

Eugenia Rho is an Assistant Professor in the Department of Computer Science at Virginia Polytechnic Institute and State University (Virginia Tech), part of the College of Engineering. Her research focuses on data analytics, machine learning, natural language processing, and human-computer interaction, with particular emphasis on social media discourse, online identity dynamics, and ethical AI applications. Education includes a Ph.D. in Information and Computer Sciences from the University of California, Irvine (2020), and a B.A. in Political Science from Columbia University (2011). Her interdisciplinary background bridges computer science and social sciences. Research interests span AI-assisted communication tools, counterspeech strategies for online hate mitigation, and neurodivergent perspectives in technology design. Her work often integrates computational methods with social science theories to address real-world challenges such as bias detection, mental health support, and ethical AI deployment. Recent publications highlight themes like AI collaboration in writing, identity-driven online interactions, and the efficacy of counterspeech. Her projects frequently involve designing human-centered technologies that prioritize accessibility and ethical considerations. No scientific awards are explicitly mentioned in the provided text. She maintains an active Google Scholar profile and a personal homepage (URLs not provided in the text).

Ying CaiView profile

Associate Professor

• AI
• Machine Learning
• Data Science
• +4 more

Ying Cai is an Associate Professor in the Department of Computer Science at Iowa State University, joining in 2003 after earning his Ph.D. in Computer Science from the University of Central Florida (2002). His research focuses on AI, machine learning, data science, cybersecurity, privacy protection, and database systems. He leads projects funded by the Air Force Research Laboratory, including work on authentication data structures for rank-aware queries, requiring U.S. citizenship and expertise in linear algebra/cryptography. Dr. Cai’s work spans cybersecurity (e.g., adversarial example defense, secure secret sharing), spatio-temporal systems (e.g., traffic risk prediction, check-in time modeling), and healthcare AI (e.g., cervical spine diagnosis with transformers). His publications emphasize practical applications of ML in privacy, security, and distributed systems. Professional roles include Associate Editor for Multimedia Tools and Applications (since 2009), Co-chair for COMPSAC TAIN/NCIW symposium (2014–2017), and TPC Chair for Mobilware 2010. His service includes contributions to INFOCOM, ICDCS, and MDM conferences. Current research opportunities exist for graduate students with strong programming/math skills, particularly in cryptography and linear algebra. He emphasizes interdisciplinary work, such as bridging AI with social sciences via large language models.

Véronique PerdereauView profile

Professor

• Robotics
• Dexterous Manipulation
• Tactile Feedback Systems
• +4 more

Véronique Perdereau is a Full Professor at Sorbonne Université's Institute of Intelligent Systems and Robotics (ISIR) , specializing in robotics and dexterous manipulation. She leads the ASIMOV research team and serves as a principal investigator for multiple European projects, including SOFTMANBOT, INDEX, and CORSMAL, focusing on tactile feedback, multimodal control, and human-robot interaction. Role: Full Professor (2024) Email: veronique.perdereau@sorbonne-universite.fr Office: H13, Campus Pierre et Marie Curie, Paris Research Focus Perdereau's work centers on tactile-driven robotic control systems, human-inspired manipulation strategies, and multimodal fusion for industrial automation. Her research bridges theoretical modeling (e.g., Cosserat mechanics, dual quaternions) with practical applications in manufacturing sectors. Key Themes: Tactile Feedback Systems Dexterous In-Hand Manipulation Human Motion Data Integration Autonomous Grasp Stability Flexible Object Handling Reproducibility in Robotic Experiments Notable Projects She has spearheaded projects like: SOFTMANBOT (2019–2023): Advanced robotic technology for handling soft materials in manufacturing. INDEX (2019–2022): Developing motor action dictionaries for robotic hands. CORSMAL (2019–2022): Multimodal fusion for collaborative object recognition. Scientific Leadership Perdereau has led work packages in 10+ international collaborations with institutions across Europe, including Decathlon, IIT, and Queen Mary University. Her research emphasizes safety, ethics, and industry 4.0 applications.

David SarpongView profile

Professor

• Strategy
• Innovation Management
• Technology Management
• +5 more

Professor David Sarpong is a leading academic in Strategy and Organisation at Aston Business School, part of Aston University's College of Business and Social Sciences. He currently serves as Head of the Marketing and Entrepreneurship Department and Director of Research for the Marketing and Strategy Subject Group. His research focuses on strategy-as-practice, relationalism, innovation management, and temporality in organizational processes. He holds adjunct roles at KNUST Business School and Bristol Business School, and is actively involved in professional organizations like EURAM and CABS. His work employs qualitative methods including ethnographic interviews and microstoria approaches. Professional Activities: He serves as Editor-in-Chief of the Journal of Strategy and Leadership, and on editorial boards for Technology Analysis & Strategic Management and European Management Journal. His external roles include UK Country Representative for EURAM and Board Membership at The Milestones Trust. He has held prior academic leadership positions at Brunel University London and visiting roles at HSE Moscow and University of KwaZulu-Natal. Research Interests: Prioritizes cross-level management problems using Heideggerian-Wittgensteinian frameworks. Key themes include second-order technology management, business tournament rituals, strategic foresight, and discursive practices in European cosmopolitan marketplaces. Methodologically innovative in combining narratives, digital datasets, and performative routines analysis. Collaborations: Active in global research networks with scholars across Africa, Europe, and Asia. Recent work addresses mining-industry partnerships, gender dynamics in extractive economies, and ethical dimensions of corporate governance. His research outputs number over 110 publications in top journals like Technovation and Journal of Business Research. Awards/Affiliations: Recognized through editorial leadership roles and visiting professorships. His research has policy implications for sustainable development, innovation ecosystems, and organizational resilience in global contexts.

Patrick SturtView profile

Associate Professor

• Psycholinguistics
• Syntactic processing
• Language comprehension
• +5 more

Patrick Sturt is a Reader in Psychology at the University of Edinburgh's School of Philosophy, Psychology and Language Sciences. His research focuses on syntactic processing in language comprehension, computational models of incremental parsing, anaphor resolution, and eye movements in reading. With over 100 publications and more than 3,300 citations, he is a recognized expert in psycholinguistics and language processing. Dr. Sturt's research interests span multiple areas of language processing. He investigates how humans comprehend sentences in real-time, with particular focus on syntactic structures, agreement phenomena, and anaphoric reference. His work often employs eye-tracking methodologies to examine the moment-by-moment processing of linguistic information. He has made significant contributions to understanding how readers handle syntactic ambiguities, garden-path sentences, and the role of prediction in language comprehension. His recent publications demonstrate a strong focus on cross-linguistic studies, particularly examining language processing in Mandarin Chinese and Korean. Many of his studies investigate how syntactic and semantic information interact during comprehension, and how linguistic structures like honorifics, classifiers, and non-canonical word orders are processed. His work bridges theoretical linguistics with experimental psycholinguistics, providing empirical evidence for models of sentence processing. Dr. Sturt actively supervises PhD students including Carine Abraham, Wenjia Cai, Chiuchou Hao, Ruomeng Zhu, and Christy Gu. He teaches Psychology of Language 1 and 2 at the MSc level, as well as Data Analysis for Psychology in R for first-year undergraduates. His teaching reflects his research expertise, providing students with both theoretical knowledge and practical analytical skills. Based in Room G29 of the Psychology Building at 7 George Square, Edinburgh, Dr. Sturt maintains regular office hours on Tuesdays from 3-4pm, providing accessibility to students and colleagues. His email address is patrick.sturt@ed.ac.uk.

• Political Economy
• Environmental Economics
• Insurance Economics
• +5 more

Professor Paul A. Raschky is affiliated with the Department of Economics at Monash University , where he also directs the SoDa Labs and co-founded the KASPR Datahaus PTY LTD and IP Observatory . His academic focus spans Political Economy , Environmental Economics , Insurance Economics , and Development Economics , with a specialization in natural hazards and data science applications. Education: PhD (University of Innsbruck, 2008), with a postdoctoral research visit at the Wharton School . Research: Explores intersections of disaster risk , governance , and economic development , leveraging machine learning and big data . Recent Publications: Focus on generative AI productivity , ethnic favoritism , disaster insurance , and conflict economics . Awards: Recipient of the ABDC Award for Innovation and Excellence in Research (2022) and Dean’s Excellence in Research Award (2016) . Projects: Leads initiatives on internet suppression in Myanmar , news media availability , and AI-driven policy insights in South-East Asia and the Pacific. Future Work: Will be on sabbatical in 2025 , likely expanding on AI applications and disaster resilience .

Eshed Ohn-BarView profile

Assistant Professor

• Machine Learning
• Autonomous Systems
• Computer Vision
• +4 more

Eshed Ohn-Bar is an Assistant Professor in the Department of Electrical & Computer Engineering at Boston University. He leads the Human-to-Everything (H2X) Lab, focused on developing intelligent systems for assistive and autonomous technologies. His research bridges machine perception, learning, and human-computer interaction, with applications in autonomous driving and accessibility for visually impaired individuals. Educated at UCLA (BS in Mathematics, 2010; MEd, 2011) and UCSD (PhD in Electrical Engineering, 2017), he holds a Humboldt Fellowship and has received the IEEE ITS Society Best PhD Dissertation Award (2017) and the 2025 BU Early Career Excellence in Research Award. His work emphasizes robust autonomy, real-time assistance, and inclusive design, collaborating with industry partners like Motional and receiving NSF grants (e.g., IIS-2152077). Research interests include autonomous systems, computer vision, and assistive technologies. Recent trends in publications highlight advancements in decision-making frameworks, neural volumetric models, and scalable learning for navigation. His lab’s projects address challenges in accessibility, such as blind motion generation and inclusive autonomous vehicle design. Awards: Humboldt Fellowship, IEEE ITS Best Dissertation, BU Early Career Award Grants: NSF IIS-2152077 Labs/Teams: H2X Lab, collaborating on projects with industry and academic partners

• Marketing Analytics
• Electronic Commerce
• Online Advertising
• +8 more

Prof. Dr. Bernd Skiera is a leading Marketing Professor at Goethe University Frankfurt since 1999 and a member of the managing board of the efl - The Data Science Institute. His work bridges information systems and marketing, with a focus on data-driven decision making and digital transformation.

Maurice SmithView profile

Professor

• Motor Control
• Sensorimotor Learning
• Neuromotor Adaptation
• +3 more

Maurice Smith serves as the Gordon McKay Professor of Bioengineering at Harvard University's School of Engineering and Applied Sciences (SEAS), where he leads the Neuromotor Control Lab. His primary appointment resides within the Department of Bioengineering, focusing on the computational and neural mechanisms underlying human movement control. Smith's research centers on sensorimotor learning , motor adaptation , and neuromotor control systems . He investigates how the brain forms and retains motor memories, particularly examining cerebellar contributions to long-term sensorimotor memory and the dissociation between implicit and explicit learning pathways. His work frequently employs computational modeling to dissect neural tuning properties and motor variability regulation. Analysis of his recent publications reveals a strong emphasis on temporal dynamics in motor learning , cerebellar function in memory consolidation , and Bayesian frameworks for understanding sensorimotor adaptation . His research demonstrates consistent focus on how error processing, uncertainty, and neural plasticity shape motor memory formation across multiple timescales. Smith maintains active collaborations with researchers including Wilsaan M. Joiner, Yohsuke R. Miyamoto, and Nathan Sandholtz, as evidenced by frequent co-authorship patterns. His laboratory investigates fundamental questions in motor control with implications for neurorehabilitation and adaptive robotics.